Zhipu is testing the same assumption DeepSeek tested a year and a half ago: that American frontier labs have an uncatchable lead. Zhipu +15%, Minimax (another Chinese AI lab) +24% overnight in HK While US AI names are falling today: Google -6.5%, Amazon -4.3%, Meta -2.6% @satyanadella summed up the shift:

yeah this actually looks really bad for zhipu/glm given the recent situation, so far no response from their official account neither Zibo @ZixuanLi_ or @louszbd respond, probably their first crisis PR.

The tragic thing is that when ZhipuSeek V5.2 Al Dente or KimiMax K4.1 Flash [in Swarm Mode] or whatever other open model becomes capable of independently solving Navier-Stokes, nobody will credit it, because of course it had seen this in training data. You can't do this later.

The founder of Chinese AI lab Zhipu argued that frontier AI should remain broadly accessible rather than controlled by select individuals, weighing in on a growing debate about the risks posed by ever more powerful models.

JUST IN: 🇨🇳 China’s Zhipu AI reportedly matches Anthropic’s Claude Mythos in security bug detection performance​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​.

New model drop in Sider: GLM-5 is here. Zhipu AI's latest flagship brings 744B parameters, a 200K context window, and frontier-level coding & reasoning, now live in your sidebar. One more reason you'll never need to leave your browser.

⚔ GLM-5.3-Flash Aims Straight at the Post-Hike DeepSeek Zhipu's GLM-5.3-Flash — revealed this week as the anonymous "Ox Alpha" — has been open-weighted and priced at roughly one-tenth of GLM-5.3. Much of the early discussion compares it to DeepSeek's V4 Flash, which recently raised prices. Zhihu contributor 起步十档, who ran Ox Alpha inside real workflows before the reveal, gives a practitioner's verdict in one line: it is built to kill the post-hike DeepSeek. His case rests on three legs — performance, token efficiency, and an architecture change that makes the price possible. 1️⃣ It clears the bar for long-horizon work Official scores put Flash between Grok 4.6 and GLM-5.3, and clearly ahead of DeepSeek V4 Flash. In the author's own testing, its frontend ability roughly matches an early gray-test build of DeepSeek V4 Pro, while its backend is noticeably weaker than GLM-5.3 — but still usable on long-horizon tasks as long as the connection holds. His rule of thumb: any model past the Claude Opus 4.6 line is workflow-ready for long tasks. Beyond that, differences come down to reasoning style and accuracy, not viability. One caveat he flags: during the anonymous test the deployment was unstable, and some believe it served a mid-training checkpoint rather than the final model. 2️⃣ The real weapon: token efficiency Comparing peak API prices against the post-hike DeepSeek V4 Flash, the author notes cached input is actually 2x more expensive, while regular input and output sit at roughly 26% of DeepSeek's price. Since cached input is often the bulk of the bill, he wants real-world tests before calling a winner on price alone. But his own usage points the same direction. In one to two hours of real work — reading and writing files, running tests — Ox Alpha burned barely over 100K tokens. He estimates DeepSeek would need 250-300K for the same workload. His prediction: same tasks, run on both APIs, will come out cheaper on Flash — with clearly better performance. 3️⃣ His unexpected advice: skip the Coding Plan The plan only triples your quota for Flash. Using Zhipu's own best-case math — maximum usage, off-peak hours, the official 0.8x API-equivalent rate — the plan works out to about 53% of pay-as-you-go API cost. Since that scenario is already extreme, he concludes the API is the better deal for almost everyone. 4️⃣ The architecture change behind the price From GLM-5 through 5.3, Zhipu used DSA — essentially an optimized full attention — which costs more than DeepSeek's CSA/HCA, Kimi's KDA, or Qwen's linear-global hybrid. That is why GLM used to be pricier than larger DeepSeek models. Flash is the first GLM to switch to linear attention plus an HCA-like compressed attention, trained with the HCA recipe as well. That brings it in line with mainstream domestic practice — and the price fell accordingly. The author expects a future GLM-5.5 can scale up without costing much more than 5.3. On top of that, Zhipu added native multimodality and leaned on domestic compute, which he reads as the reason for the generous free quotas during the anonymous test. 5️⃣ Zhipu is still the team to beat The author's closing line is unambiguous: Zhipu remains, in his words, the number-one Chinese model company. That is his judgment, not a benchmark result — but the cost argument underneath it is now easy to check yourself. 🔗 Key links: Official announcement: Open weights (MIT): 🔗 Full Reading: #GLM# #Zhipu# #DeepSeek# #LLM# #AIInference# #TokenEfficiency# #OpenWeights#

🔌 GLM-5.3-Flash Served Its Viral Debut Entirely on Domestic Chinese Chips Zhipu's GLM-5.3-Flash — the 320B-A18B model revealed this week as the anonymous "Ox Alpha" — set usage records on OpenRouter and OpenCode during its undercover test. The company says all of that traffic was served by domestic Chinese chip clusters. Zhihu contributor 刘延 reconstructs how Zhipu lined up this infrastructure, and reads the official engineering details for hints about which chips are actually doing the work. The core judgment: the bigger story here is not the model itself, but that a frontier-level model handled global-scale, real-world inference on domestic silicon. 1️⃣ The timeline behind the launch The author pieces together a sequence from public reporting. 🔹 Zhipu was reported to have acquired an infrastructure company. 🔹 LatePost reported Zhipu had brought 50,000 domestic cards online; around the same time, its CodePlan subscription got cheaper with generous bonus quotas. 🔹 Ox Alpha went live anonymously and, in the author's words, blew up worldwide. 🔹 Zhipu then confirmed every request in that test ran on domestic chips. 🔹 The latest LatePost report puts the deployment at 100,000 domestic cards. Note the card counts come from media reports, not Zhipu itself. 2️⃣ The engineering: surviving 1M context on constrained hardware Zhipu's own statement is unusually specific about the constraints. The main bottleneck on these chips is memory capacity and bandwidth, and supporting a 1M-token context is the hardest part. The company's listed optimizations include trading compute for bandwidth and communication for memory, intra-node tensor parallelism for the linear attention and the LM head, ReplaySSM, W8A8 quantization, INT8/FP8/BF16 mixed cache quantization, and Layer Split. 3️⃣ Which chips? Reading the precision hints Here the author speculates, and it should be read as inference, not confirmation. 🔹 FP8 support suggests Moore Threads could be handling prefill, or possibly Hygon's DCU-3. 🔹 INT8 points toward Ascend 910B/C as the likely backbone. 🔹 No mention of FP4 suggests the newer Ascend 950 is probably not in the mix. 4️⃣ Why this matters If the reporting holds, this is the first time domestic Chinese chip clusters have carried a frontier model's global production traffic at this scale — including a free, record-breaking stress test from developers worldwide. The author treats it as a proof point: China's domestic chips are no longer just for training experiments or internal pilots, but can serve a top-tier model to the open internet. 🔗 Key links: Official announcement: Open weights (MIT): 🔗 Full Reading: #GLM# #Zhipu# #AIChips# #AIInfra# #Ascend# #Semiconductors# #OpenWeights#